CN113538041B - Power package recommendation method and device based on load curve clustering analysis - Google Patents

Power package recommendation method and device based on load curve clustering analysis Download PDF

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CN113538041B
CN113538041B CN202110730352.2A CN202110730352A CN113538041B CN 113538041 B CN113538041 B CN 113538041B CN 202110730352 A CN202110730352 A CN 202110730352A CN 113538041 B CN113538041 B CN 113538041B
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electricity
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CN113538041A (en
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杨威
曾智健
赵俊华
陈战林
赵焕
何秉昊
王馨蕾
丁绮璐
龚学良
刘嘉逊
吴敬慧
杨柳
张�杰
李凯欣
张朋宇
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Guangdong Electric Power Transaction Center Co ltd
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    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0201—Market modelling; Market analysis; Collecting market data
    • G06Q30/0202—Market predictions or forecasting for commercial activities
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/23—Clustering techniques
    • G06F18/232—Non-hierarchical techniques
    • G06F18/2321—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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    • G06Q30/06—Buying, selling or leasing transactions
    • G06Q30/0601—Electronic shopping [e-shopping]
    • G06Q30/0631—Item recommendations
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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Abstract

The embodiment of the invention discloses a power package recommendation method based on load curve clustering analysis, which comprises the following steps: acquiring historical electricity utilization data of terminal users, and analyzing a typical electricity utilization mode of each terminal user by using a DBSCAN clustering algorithm; optimizing the combination in the typical electricity utilization mode of the terminal user according to a time-of-use electricity price structure and an electricity price level by using a mixed integer nonlinear programming method, and determining an optimal electricity utilization mode package; recommending the optimal power consumption mode package for each terminal user to the terminal user. The method can effectively reduce the electricity cost of the terminal user while ensuring the return rate of the retailer under the constraint of considering the elastic constraint of the power demand and the operation of the power distribution network, and improves the competitiveness of the retailer in the market.

Description

Power package recommendation method and device based on load curve clustering analysis
Technical Field
The embodiment of the invention belongs to the field of electricity, and particularly relates to a method and a device for recommending a power package based on load curve clustering analysis.
Background
As the electricity market has developed, retail electricity prices have evolved from fixed, uniform pricing to dynamic, even real-time pricing. For ordinary home users, however, this means that they will be directly faced with the risk of price fluctuations, which is unacceptable. Therefore, the time-of-use electricity prices between the fixed price and the dynamic price are widely adopted on a global scale. However, when the power retailer makes a price package of the retail market, the power retailer is often optimized only for the price level of the time-of-use electricity price based on the overall behavior of the market, so as to maximize the income of the power retailer. However, such retail price packages do not take into account the different electricity usage patterns of the different end users on the one hand, nor the ladder structure of the time of use prices, thereby neglecting the complementarity between the different end users on the time scale.
Disclosure of Invention
In order to solve the above technical problems, embodiments of the present invention provide a method and an apparatus for recommending a power package based on load curve clustering analysis:
a power package recommendation method based on load curve clustering analysis comprises the following steps:
obtaining historical electricity utilization data of terminal users, and analyzing a typical electricity utilization mode of each terminal user by using a DBSCAN clustering algorithm;
optimizing the combination in the typical electricity utilization mode of the terminal user according to a time-of-use electricity price structure and an electricity price level by using a mixed integer nonlinear programming method, and determining an optimal electricity utilization mode package;
recommending the optimal power consumption mode package for each terminal user to the terminal user.
A power package recommendation device based on load curve cluster analysis comprises:
the system comprises an acquisition module, a data processing module and a data processing module, wherein the acquisition module is used for acquiring historical electricity consumption data of terminal users and analyzing a typical electricity consumption mode of each terminal user by using a DBSCAN clustering algorithm;
the processing module is used for optimizing the combination in the typical electricity utilization mode of the terminal user according to the time-of-use electricity price structure and the electricity price level by using a mixed integer nonlinear programming method, and determining an optimal electricity utilization mode package;
and the execution module is used for recommending the optimal power consumption mode package aiming at each terminal user to the terminal user.
The embodiment of the invention has the beneficial effects that: analyzing a typical power utilization mode based on a DBSCAN clustering algorithm and historical power utilization data of the terminal users, and determining the power utilization mode of each terminal user; and for different power utilization modes, optimizing the stepped structure and the power price level of the time-of-use power price by using a mixed integer nonlinear programming method so as to minimize the power utilization cost of the terminal user under the constraint of ensuring the return rate of retailers and recommend the package with the minimum power utilization cost to the user. Under the constraint of considering the elastic constraint of power demand and the constraint of power distribution network operation, the method can effectively reduce the power consumption cost of the terminal user while ensuring the return rate of the retailer, and improve the competitiveness of the retailer in the market. The method can be applied to transaction service charging analysis of the future electric power retail market by combining terminal user clustering and power utilization pattern analysis, and assists the electric power retail market to formulate retail price packages. The invention learns the user load composition and the power utilization behavior rule through data mining, and can be applied to customer management strategy formulation, power selling decision optimization and customized service differentiation of power selling companies.
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In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic flow chart of a power package recommendation method based on load curve cluster analysis according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of an electrical configuration provided by an embodiment of the present invention;
FIG. 3 is a schematic diagram provided by an embodiment of the present invention;
FIG. 4 is another schematic diagram provided in accordance with an embodiment of the present invention;
FIG. 5 provides another schematic representation of an embodiment of the present invention;
fig. 6 is a block diagram of a basic structure of an electric power package recommendation device based on load curve cluster analysis according to an embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention.
In some of the flows described in the present specification and claims and in the above figures, a number of operations are included that occur in a particular order, but it should be clearly understood that these operations may be performed out of order or in parallel as they occur herein, with the order of the operations being indicated as 101, 102, etc. merely to distinguish between the various operations, and the order of the operations by themselves does not represent any order of performance. Additionally, the flows may include more or fewer operations, and the operations may be performed sequentially or in parallel. It should be noted that, the descriptions of "first", "second", etc. in this document are used for distinguishing different messages, devices, modules, etc., and do not represent a sequential order, nor limit the types of "first" and "second" to be different.
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, fig. 1 is a diagram illustrating a method for recommending a power package based on load curve clustering analysis according to an embodiment of the present invention, where the method specifically includes the following steps:
s110, obtaining historical electricity utilization data of terminal users, and analyzing a typical electricity utilization mode of each terminal user by using a DBSCAN clustering algorithm;
s120, optimizing the combination in the typical electricity utilization mode of the terminal user according to a time-of-use electricity price structure and an electricity price level by using a mixed integer nonlinear programming method, and determining an optimal electricity utilization mode package;
and S130, recommending the optimal power utilization mode package for each terminal user to the terminal user.
The electric power package recommending device based on load curve clustering analysis in the embodiment of the invention analyzes a typical power consumption mode based on a DBSCAN clustering algorithm and historical power consumption data of terminal users, and determines the power consumption mode of each terminal user; and for different power utilization modes, optimizing the stepped structure and the power price level of the time-of-use power price by using a mixed integer nonlinear programming method so as to minimize the power utilization cost of the terminal user under the constraint of ensuring the return rate of retailers and recommend the package with the minimum power utilization cost to the user. Under the constraint of considering the elastic constraint of power demand and the constraint of power distribution network operation, the method can effectively reduce the power consumption cost of the terminal user while ensuring the return rate of the retailer, and improve the competitiveness of the retailer in the market. By combining the clustering of the terminal users and the analysis of the power utilization mode, the method can be applied to transaction service charge analysis of the future power retail market and assist the power retail market in formulating retail price packages. The invention learns the user load composition and the power utilization behavior rule through data mining, and can be applied to customer management strategy formulation, power selling decision optimization and customized service differentiation of power selling companies.
The embodiment of the invention provides a method for calculating the power utilization mode of the terminal user by utilizing a DBSCAN clustering algorithm formula
Figure GDA0003738493310000051
Comprises the following steps:
Figure GDA0003738493310000052
wherein the vector
Figure GDA0003738493310000053
Represents the electricity consumption vector of the terminal user j in tau days, n (-) represents the standardization operation,
Figure GDA0003738493310000054
denotes a ratio of power usage by said end user j for a time period t times t days over the user's total day power usage on that day, H denotes a time period of historical power usage data, TPS denotes a set of typical power usage patterns, TP denotes a typical power usage pattern for an end user, | | 2 Representing the L2 norm of the vector.
The embodiment of the invention provides a method for optimizing the power utilization mode of a terminal user according to a time-of-use power price structure and a power price level by using an optimized objective function of mixed integer nonlinear programming;
the optimization objective function is: minS tp ＝∑ j∈J ∑ t∈T E(L j,t )·f j,t (r j,t )·r j,t
Wherein S is tp Representing a total electricity usage cost for the end user; j represents the set of all end users; t represents a decision period; e (L) j,t ) Representing a desire of the end user j to use electricity during a t-th time period of the day;r j,t represents the electricity price of the end user j in the t-th time period of each day, f j,t (. Cndot.) represents the elasticity of the end user j's power demand with respect to real-time electricity prices at the t-th time period of each day.
The embodiment of the invention provides a method for calculating the power utilization mode of a terminal user by using a DBSCAN clustering algorithm formula, which comprises the following steps:
step 1, standardizing historical electricity consumption data of the terminal user
Figure GDA0003738493310000055
Figure GDA0003738493310000056
Domain parameters (ε, minPts);
step 2, initializing a core object set
Figure GDA0003738493310000057
Initializing the number of typical power patterns k =0, initializing the set of unaccessed data
Figure GDA0003738493310000061
Initializing a set of typical power usage patterns
Figure GDA0003738493310000062
Step 3, for J 'belonging to J and tau' belonging to H, searching
Figure GDA0003738493310000063
Epsilon-domain subsamples set of
Figure GDA0003738493310000064
Figure GDA0003738493310000065
If it is
Figure GDA0003738493310000066
Number of samples of the epsilon-Domain subsample set (denoted as
Figure GDA0003738493310000067
) Satisfy the requirement of
Figure GDA0003738493310000068
Then will be
Figure GDA0003738493310000069
Adding a core object set:
Figure GDA00037384933100000610
obtaining a core object set;
step 4, if the core object set
Figure GDA00037384933100000611
Then the average of the normalized end-user historical electricity usage data is added to the set of typical electricity usage patterns:
Figure GDA00037384933100000612
Figure GDA00037384933100000613
finishing the algorithm;
step 5, if the core object set
Figure GDA00037384933100000614
Randomly selecting a core object o from a core object set omega, and initializing a current cluster core object queue omega cur = o, updating the number of typical electricity usage patterns k = k +1, initializing the current cluster sample set C k = { o }, update unvisited sample set Γ = Γ - { o };
step 6, if the current cluster core object queue
Figure GDA00037384933100000615
Then the current cluster C is clustered k After the generation is finished, the typical electricity utilization mode set is updated
Figure GDA00037384933100000616
Updating the set of core objects Ω = Ω -C k Turning toEntering a step 5; if the current cluster core object queue
Figure GDA00037384933100000617
Then the core object set Ω = Ω -C is updated k ，
Step 7, in the current cluster core object queue omega cur Taking out a core object o', finding out epsilon-field subsample set N of o ε (o') let Δ = N ε (o') # Γ, updating the current cluster sample set C k ＝C k And U delta, updating an unvisited sample set gamma = gamma-delta and updating omega cur ＝Ω cur U (. DELTA.andgate.OMEGA) -o' is transferred to step 6.
And 8, outputting and initializing a typical power consumption mode set TPS.
When the power consumption mode of the terminal user is optimized according to the time-of-use power price structure and the power price level, modeling is carried out on the power purchase cost, the risk price premium and the expected retail income according to the input expected return rate, the time-of-use power price step number, the daily decision period, the number and price for signing the long-term contract, the power price elastic equation coefficient, the risk weighting factor, the risk price and the spot price expectation, so that a power purchase cost model, a risk price premium model and an expected retail income model are obtained, and a constraint model is constructed, so that a time-of-use power price constraint model, a power demand elastic constraint model and a power distribution network operation constraint model are obtained.
The embodiment of the invention provides a method for optimizing a power utilization mode of a terminal user according to a time-of-use power price structure and a power price level by using an optimization objective function of mixed integer nonlinear programming, wherein the power utilization mode of the terminal user is optimized according to the time-of-use power price structure and the power price level by using a time-of-use power price constraint model, a power demand elastic constraint model and a power distribution network operation constraint model;
wherein the time-of-use electricity price constraint model is as follows:
Figure GDA0003738493310000071
y i,j ＝(y i,1,j ,y i,2,j ,…,y i,t,j ,…,y i,|T|,j )
Figure GDA0003738493310000072
Figure GDA0003738493310000073
wherein N is pb A step number representing a time of use electricity price for the retail price package; y is i,t,j Is a boolean variable whose value is 1 or 0, indicating whether the ith electricity price block covers t of the day, if so, y i,t,j =1, otherwise y i,t,j ＝0；
Figure GDA0003738493310000081
Is the electricity purchase cost of the retailer for customer j at i, including costs from forward contracts and spot markets;
Figure GDA0003738493310000082
is a risk premium for user j for the retailer.
The elastic constraint model of the power demand is as follows:
Figure GDA0003738493310000083
Figure GDA0003738493310000084
Figure GDA0003738493310000085
wherein r is 0,t Is a nominal retail price; beta is a 0,j ，β 0,j And beta 0,j Are parameters.
The power distribution network operation constraint model is as follows:
Figure GDA0003738493310000086
U i,j ＝(u i,1,j ,u i,2,j ,…,u i,|T|,j )
V i,j ＝(v i,1,j ,v i,2,j ,…,v i,|T|,j )
Figure GDA0003738493310000087
Figure GDA0003738493310000088
Figure GDA0003738493310000089
where ρ is l,k Is the power distribution coefficient, representing the relative change in active power that occurs on line l due to the actual power change at node k;
Figure GDA00037384933100000810
is the power limit of line l; k and L are respectively a node set and a line set of the power distribution network; u shape i,j And V i,j Are binary vectors whose elements are 0 or 1.
The embodiment of the invention provides a method for determining an optimal power consumption mode package, which comprises the following steps:
determining the optimal power utilization pattern package by utilizing a preset end user power utilization expectation model, a retailer power purchase cost model, a risk price premium model in the retail price and an expected retail income model of the retailer;
wherein the end user's electricity usage expectation model:
Figure GDA0003738493310000091
wherein the content of the first and second substances,
Figure GDA0003738493310000092
the t-th element of the power usage pattern of end user j.
The electricity purchase cost model of the retailer is as follows:
Figure GDA0003738493310000093
Figure GDA0003738493310000094
Figure GDA0003738493310000095
Figure GDA0003738493310000096
L j,t ＝n(E(L j,t ))·E(Q j )
wherein N is F Is the number of forward contracts the retailer has signed;
Figure GDA0003738493310000097
and
Figure GDA0003738493310000098
respectively indicating the number and price level of the retailer's contracted forward contracts m; beta is a fc Is a weighted factor, β, between the expected revenue and profit risk of the retailer fc ∈[0,+∞)；
Figure GDA0003738493310000099
Representing retail risks arising from differences between the forward contract and the end user load expectations; alpha is used to calculate the conditional value-at-risk, CVaR) value-at-risk (VaR); β is a given confidence level; n is a radical of S Represents the number of samples;
Figure GDA0003738493310000101
is the cost of electricity purchased by the retailer from the spot market;
Figure GDA0003738493310000102
is a binary variable, if
Figure GDA0003738493310000103
Representing a lead time belonging to a forward contract m at t;
Figure GDA0003738493310000104
is the spot price at t; e (L) j,t ) The desire of the time end user j to use electricity at time t; e (Q) j ) Indicating the end user j's desire for daily electricity usage.
A risk premium model in the retail price:
Figure GDA0003738493310000105
the expected retail revenue model for the retailer:
Figure GDA0003738493310000106
Figure GDA0003738493310000107
wherein W represents the retail profit of the retailer; e represents the profitability of the retailer's demand.
The embodiment of the invention also comprises the following steps:
linearize retail risk due to differences between the forward contract and the end user load expectations:
Figure GDA0003738493310000108
Figure GDA0003738493310000109
M j,n ≥0
wherein, M j,n Is for linearization
Figure GDA00037384933100001010
But an auxiliary variable introduced.
Referring to fig. 2, the power distribution network has 37 network nodes in total. The system has 31 terminal users, 5 feeders, and the data and parameters of the feedback feeder are shown in table 1. In an embodiment of the present invention, referring to fig. 3, the L2 norm distance value between samples of the normalized end-user historical electricity consumption data is mainly distributed between 0 and 0.2. The historical electricity consumption data contains 2790 daily electricity consumption data for a total of 31 end users for 90 days.
TABLE 1. Mini Engine parameters
Figure GDA0003738493310000111
By selecting epsilon =0.2 and minpts =300 as the domain parameters, 7 typical power usage patterns can be obtained by the method of the present invention, please refer to fig. 4. The load peaks for typical power usage patterns 1 and 3 occur at 20:00 to 21:00, the peak load of typical power usage pattern 4 occurs in the early morning of each day, typical power usage patterns 2, 5, 7 are all load patterns that include two peak load periods, and the two peak load periods are located early in the morning and late afternoon, respectively, and the total daily power usage of typical power usage pattern 6 remains relatively stable. See fig. 5 for an example of a retail price package for an end user obtained by the method of the present invention.
As shown in fig. 6, to solve the above problem, an embodiment of the present invention further provides an electric power package recommendation apparatus based on load curve cluster analysis, including: the system comprises a fetching module 2100, a processing module 2200 and an executing module 2300, wherein the fetching module 2100 is used for obtaining historical electricity utilization data of the terminal users and analyzing a typical electricity utilization mode of each terminal user by using a DBSCAN clustering algorithm; a processing module 2200, configured to optimize a combination in a typical electricity consumption mode of the end user according to a time-of-use electricity price structure and an electricity price level by using a mixed integer nonlinear programming method, and determine an optimal electricity consumption mode package; an executing module 2300, configured to recommend the optimal power usage pattern package for each end user to the end user.
The electric power package recommending device based on load curve clustering analysis in the embodiment of the invention analyzes a typical power consumption mode based on a DBSCAN clustering algorithm and historical power consumption data of terminal users, and determines the power consumption mode of each terminal user; for different power utilization modes, a mixed integer nonlinear programming method is utilized to optimize the stepped structure and the power price level of the time-of-use power price, so that the power utilization cost of the terminal user is minimized under the constraint of ensuring the return rate of a retailer, and a package with the minimum power utilization cost is recommended to the user. Under the constraint of considering the elastic constraint of power demand and the constraint of power distribution network operation, the method can effectively reduce the power consumption cost of the terminal user while ensuring the return rate of the retailer, and improve the competitiveness of the retailer in the market. By combining the clustering of the terminal users and the analysis of the power utilization mode, the method can be applied to transaction service charge analysis of the future power retail market and assist the power retail market in formulating retail price packages. The invention learns the user load composition and the power utilization behavior rule through data mining, and can be applied to customer management strategy formulation, power selling decision optimization and customized service differentiation of power selling companies.
In some embodiments, the processing module 2100 is configured to calculate the power usage pattern of the end user using a DBSCAN clustering algorithm formula
Figure GDA0003738493310000121
Comprises the following steps:
Figure GDA0003738493310000122
wherein the vector
Figure GDA0003738493310000123
Represents the electricity consumption vector of the end user j in tau days, n (-) represents the normalization operation,
Figure GDA0003738493310000124
denotes a ratio of power usage by said end user j for a t-th time period on a t day to the power usage of the user all day on that day, h denotes a time period of historical power usage data, TPS denotes a set of typical power usage patterns, TP denotes a typical power usage pattern for an end user, | · | 2 Representing the L2 norm of the vector.
In some embodiments, the execution module 1300 is configured to optimize the end user's power usage pattern in terms of a time of use power rate structure and a power rate level using a mixed integer nonlinear programming optimization objective function;
the optimization objective function is: minS tp ＝∑ j∈J ∑ t∈T E(L j,t )·f j,t (r j,t )·r j,t
Wherein S is tp Representing a total electricity cost for the end user; j represents the set of all end users; t represents a decision period; e (L) j,t ) Representing a desire of the end user j to use electricity during a t-th time period of the day; r is a radical of hydrogen j,t Represents the electricity price of the end user j in the t-th time period of each day, f j,t (. H) represents the elasticity of the end user j's electricity demand with respect to real-time electricity prices at the tth time period of the day.
In some embodiments, the processing module 2200 is configured to calculate the electricity usage pattern of the end user by using the DBSCAN clustering algorithm formula, and includes:
step 1, standardizing historical electricity consumption data of the terminal user
Figure GDA0003738493310000131
Figure GDA0003738493310000132
Domain parameters (ε, minPts);
step 2, initializing a core object set
Figure GDA0003738493310000133
Initializing the number of typical power patterns k =0, initializing the set of unaccessed data
Figure GDA0003738493310000134
Initializing a set of typical power usage patterns
Figure GDA0003738493310000135
Step 3, for J 'epsilon J and tau' epsilon H, searching
Figure GDA0003738493310000136
Epsilon-field subsample set of
Figure GDA0003738493310000137
Figure GDA0003738493310000138
If it is
Figure GDA0003738493310000139
Number of samples of the epsilon-Domain subsample set (denoted as
Figure GDA00037384933100001310
) Satisfy the requirement of
Figure GDA00037384933100001311
Then will be
Figure GDA00037384933100001312
Adding a core object set:
Figure GDA00037384933100001313
obtaining a core object set;
step 4, if the core object set
Figure GDA00037384933100001314
Then the average of the normalized end-user historical electricity usage data is added to the set of typical electricity usage patterns:
Figure GDA00037384933100001315
Figure GDA0003738493310000141
finishing the algorithm;
step 5, if the core object set
Figure GDA0003738493310000142
Randomly selecting a core object o from a core object set omega, and initializing a current cluster core object queue omega cur = o, updating the number of typical electricity usage patterns k = k +1, initializing the current cluster sample set C k = { o }, update unvisited sample set Γ = Γ - { o };
step 6, if the current cluster core object queue
Figure GDA0003738493310000143
Then the current cluster C is clustered k After the generation is finished, the typical electricity consumption mode set is updated
Figure GDA0003738493310000144
Updating the set of core objects Ω = Ω -C k Turning to step 5; if the current cluster core object queue
Figure GDA0003738493310000145
Then the core object set Ω = Ω -C is updated k ，
Step 7, in the current cluster core object queue omega cur Taking out a core object o', finding out epsilon-field subsample set N of o ε (o') let Δ = N ε (o') # Γ, update the current cluster sample set C k ＝C k And U delta, updating an unvisited sample set gamma = gamma-delta and updating omega cur ＝Ω cur U.g.,. DELTA.andgate.OMEGA) -o' is carried out in step 6.
And 8, outputting and initializing a typical power utilization mode set TPS.
In some embodiments, the processing module 2200 is configured to optimize the power consumption mode of the end user according to the time-of-use power rate structure and the power rate level by using a time-of-use power rate constraint model, a power demand elasticity constraint model, and a distribution network operation constraint model;
wherein the time-of-use electricity price constraint model is as follows:
Figure GDA0003738493310000146
y i,j ＝(y i,1,j ,y i,2,j ,…,y i,t,j ,…,y i,|T|,j )
Figure GDA0003738493310000151
Figure GDA0003738493310000152
wherein, N pb A step number representing a time of use electricity price for the retail price package; y is i,t,j Is a Boolean variable, whose value is 1 or 0, which indicates whether the ith electricity price block covers t in the day, if so, y i,t,j =1, otherwise y i,t,j ＝0；
Figure GDA0003738493310000153
Is the electricity purchase cost of the retailer for customer j at i, including costs from forward contracts and spot markets;
Figure GDA0003738493310000154
is the retailer's risk premium for user j.
The elastic constraint model of the power demand is as follows:
Figure GDA0003738493310000155
Figure GDA0003738493310000156
Figure GDA0003738493310000157
wherein r is 0,t Is a nominal retail price; beta is a 0,j ，β 0,j And beta 0,j Are parameters.
The power distribution network operation constraint model is as follows:
Figure GDA0003738493310000158
U i,j ＝(u i,1,j ,u i,2,j ,…,u i,|T|,j )
V i,j ＝(v i,1,j ,v i,2,j ,…,v i,|T|,j )
Figure GDA0003738493310000159
Figure GDA0003738493310000161
Figure GDA0003738493310000162
where ρ is l,k Is the power distribution coefficient, representing the relative change in active power that occurs on line l due to the actual power change at node k;
Figure GDA0003738493310000163
is the power limit of line l; k and L being power distribution networks respectivelyA node set and a line set; u shape i,j And V i,j Are binary vectors whose elements are 0 or 1.
In some embodiments, the execution module 2300 is configured to determine the optimal power usage pattern package using a preset end-user power usage expectation model, a retailer's power purchase cost model, a risk premium model in retail price, and a retailer's expected retail revenue model;
wherein the end user's power usage expectation model is:
Figure GDA0003738493310000164
wherein, the first and the second end of the pipe are connected with each other,
Figure GDA0003738493310000165
the t-th element of the power usage pattern of end user j.
The electricity purchase cost model of the retailer is as follows:
Figure GDA0003738493310000166
Figure GDA0003738493310000171
Figure GDA0003738493310000172
Figure GDA0003738493310000173
L j,t ＝n(E(L j,t ))·E(Q j )
wherein, N F Is the number of forward contracts the retailer has signed;
Figure GDA0003738493310000174
and
Figure GDA0003738493310000175
respectively indicating the number and price level of the retailer's contracted forward contracts m; beta is a fc Is a weighted factor, β, between the expected revenue and profit risk of the retailer fc ∈[0,+∞)；
Figure GDA0003738493310000176
Representing retail risks arising from differences between forward contracts and end-user load expectations; α represents a value-at-risk (VaR) for calculating a conditional value-at-risk (CVaR); β is a given confidence level; n is a radical of hydrogen S Represents the number of samples;
Figure GDA0003738493310000177
is the cost of electricity purchased by the retailer from the spot market;
Figure GDA0003738493310000178
is a binary variable, if
Figure GDA0003738493310000179
Representing the lead time belonging to the forward contract m at t;
Figure GDA00037384933100001710
is the spot price at t; e (L) j,t ) An expectation of electricity usage by the time end user j at time t; e (Q) j ) Indicating the end user j's desire for daily electricity usage.
A risk premium model in the retail price:
Figure GDA00037384933100001711
the expected retail revenue model for the retailer:
Figure GDA00037384933100001712
Figure GDA0003738493310000181
wherein W represents the retail profit of the retailer; e represents the profitability of the retailer's demand.
In some embodiments, the processing module 1200 is further configured to linearize retail risk due to a difference between the forward contract and the end-user load expected value:
Figure GDA0003738493310000182
Figure GDA0003738493310000183
M j,n ≥0
wherein M is j,n Is for linearization
Figure GDA0003738493310000184
But an auxiliary variable introduced.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above may be implemented by a computer program, which may be stored in a computer readable storage medium, and when executed, may include the processes of the embodiments of the methods described above. The storage medium may be a non-volatile storage medium such as a magnetic disk, an optical disk, a Read-Only Memory (ROM), or a Random Access Memory (RAM).
It should be understood that, although the steps in the flowcharts of the figures are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and may be performed in other orders unless explicitly stated herein. Moreover, at least a portion of the steps in the flow chart of the figure may include multiple sub-steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, which are not necessarily performed in sequence, but may be performed alternately or alternately with other steps or at least a portion of the sub-steps or stages of other steps.
The foregoing is only a partial embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.

Claims (8)

1. A power package recommendation method based on load curve clustering analysis is characterized by comprising the following steps:
acquiring historical electricity utilization data of terminal users, and analyzing a typical electricity utilization mode of each terminal user by using a DBSCAN clustering algorithm;
optimizing the combination in the typical electricity utilization mode of the terminal user according to a time-of-use electricity price structure and an electricity price level by using a mixed integer nonlinear programming method, and determining an optimal electricity utilization mode package;
recommending the optimal power consumption mode package for each terminal user to the terminal user;
wherein, the electricity utilization mode of the end user is calculated by utilizing a DBSCAN clustering algorithm formula
Figure 401968DEST_PATH_IMAGE001
Comprises the following steps:
Figure 771769DEST_PATH_IMAGE002
wherein the vector
Figure 990261DEST_PATH_IMAGE003
Representing the end user
Figure 787316DEST_PATH_IMAGE004
In that
Figure 396152DEST_PATH_IMAGE005
The electricity consumption vector of the day is calculated,
Figure 304065DEST_PATH_IMAGE006
which represents a standardized operation of the process of the present invention,
Figure 314746DEST_PATH_IMAGE007
to (1) a
Figure 282702DEST_PATH_IMAGE008
An element representing the end user
Figure 378834DEST_PATH_IMAGE004
In that
Figure 152755DEST_PATH_IMAGE008
The first day
Figure 283522DEST_PATH_IMAGE008
The power consumption of each time period accounts for the ratio of the power consumption of the user on the day and the whole day,
Figure 422379DEST_PATH_IMAGE009
a time period representing historical electricity usage data,
Figure 740228DEST_PATH_IMAGE010
representing a collection of typical power usage patterns, TP representing a typical power usage pattern for an end user,
Figure 255523DEST_PATH_IMAGE011
representing the L2 norm of the vector.
2. The power package recommendation method according to claim 1, wherein the power usage patterns of the end users are optimized according to a time-of-use power rate structure and a power rate level using an optimization objective function of mixed integer nonlinear programming;
the optimization objective function is:
Figure 240797DEST_PATH_IMAGE012
wherein, the first and the second end of the pipe are connected with each other,
Figure 550555DEST_PATH_IMAGE013
representing a total electricity usage cost for the end user;
Figure 919482DEST_PATH_IMAGE014
represents the set of all end users;
Figure 972889DEST_PATH_IMAGE015
representing a decision cycle;
Figure 812669DEST_PATH_IMAGE016
representing the end user
Figure 293329DEST_PATH_IMAGE004
On the first day
Figure 851349DEST_PATH_IMAGE008
Expectation of electricity consumption in various time periods;
Figure 442867DEST_PATH_IMAGE017
representing the end user
Figure 465050DEST_PATH_IMAGE004
On the first day
Figure 116611DEST_PATH_IMAGE008
The electricity prices of the individual time periods,
Figure 896348DEST_PATH_IMAGE018
representing the end user
Figure 291558DEST_PATH_IMAGE004
On the first day
Figure 105930DEST_PATH_IMAGE008
Elasticity of electricity demand for each time period with respect to real-time electricity prices.
3. The power package recommendation method of claim 1, wherein said calculating the end user's power usage pattern using a DBSCAN clustering algorithm formula comprises:
step 1, standardizing historical electricity consumption data of the terminal user
Figure 928392DEST_PATH_IMAGE019
Field parameter
Figure 195426DEST_PATH_IMAGE020
；
Step 2, initializing a core object set
Figure 456643DEST_PATH_IMAGE021
Initializing typical number of power modes
Figure 125521DEST_PATH_IMAGE022
Initializing an unaccessed data set
Figure 118885DEST_PATH_IMAGE023
Initializing a set of typical power usage patterns
Figure 873215DEST_PATH_IMAGE024
；
Step 3, for
Figure 875806DEST_PATH_IMAGE025
Look up
Figure 399191DEST_PATH_IMAGE026
Is/are as follows
Figure 563456DEST_PATH_IMAGE027
-set of domain subsamples
Figure 867398DEST_PATH_IMAGE028
If at all
Figure 408101DEST_PATH_IMAGE029
Is
Figure 785993DEST_PATH_IMAGE027
-number of samples of domain subsample set
Figure 386738DEST_PATH_IMAGE030
Satisfy the requirement of
Figure 850081DEST_PATH_IMAGE031
Then will be
Figure 194474DEST_PATH_IMAGE032
Adding a core object set:
Figure 426873DEST_PATH_IMAGE033
obtaining a core object set;
step 4, if the core object set
Figure 774020DEST_PATH_IMAGE021
Then the average of the normalized end-user historical electricity usage data is added to the set of typical electricity usage patterns:
Figure 724659DEST_PATH_IMAGE034
and ending the algorithm;
step 5, if the core object set
Figure 607164DEST_PATH_IMAGE035
In the core object set
Figure 959648DEST_PATH_IMAGE036
In which a core object is randomly selected
Figure 902196DEST_PATH_IMAGE037
Initializing current cluster core object queue
Figure 340131DEST_PATH_IMAGE038
Updating the number of the typical power consumption modes
Figure 88644DEST_PATH_IMAGE039
Initializing the current cluster sample set
Figure 295634DEST_PATH_IMAGE040
Updating the set of unaccessed samples
Figure 409084DEST_PATH_IMAGE041
；
Step 6, if the current cluster core object queue
Figure 334314DEST_PATH_IMAGE042
Then cluster is currently clustered
Figure 558622DEST_PATH_IMAGE043
After the generation is finished, the typical electricity utilization mode set is updated
Figure 885698DEST_PATH_IMAGE044
Updating the core object set
Figure 904470DEST_PATH_IMAGE045
Turning to step 5; if the current cluster core object queue
Figure 379314DEST_PATH_IMAGE046
Then updating the core object set
Figure 407313DEST_PATH_IMAGE045
；
Step 7, in the current cluster core object queue
Figure 588895DEST_PATH_IMAGE047
Fetching a core object
Figure 44147DEST_PATH_IMAGE048
Find out
Figure 678391DEST_PATH_IMAGE049
Is/are as follows
Figure 244502DEST_PATH_IMAGE050
-set of domain subsamples
Figure 608487DEST_PATH_IMAGE051
Let us order
Figure 234640DEST_PATH_IMAGE052
Updating the current cluster sample set
Figure 356180DEST_PATH_IMAGE053
Updating the set of unaccessed samples
Figure 725981DEST_PATH_IMAGE054
Update
Figure 882156DEST_PATH_IMAGE055
Turning to step 6;
step 8, outputting and initializing typical power consumption mode set
Figure 679211DEST_PATH_IMAGE056
。
4. The power package recommendation method of claim 2, wherein said optimizing the end user's power usage pattern according to a time of use power rate structure and power rate level using an optimized objective function of mixed integer nonlinear programming comprises:
optimizing the power utilization mode of the terminal user according to the time-of-use power price structure and the power price level by utilizing a time-of-use power price constraint model, a power demand elastic constraint model and a power distribution network operation constraint model;
wherein the time-of-use electricity price constraint model is as follows:
Figure 288047DEST_PATH_IMAGE057
Figure 759742DEST_PATH_IMAGE058
Figure 770423DEST_PATH_IMAGE059
Figure 738379DEST_PATH_IMAGE060
wherein, the first and the second end of the pipe are connected with each other,
Figure 834511DEST_PATH_IMAGE061
a step number representing a time of use electricity price for the retail price package;
Figure 546115DEST_PATH_IMAGE062
is a Boolean variable having a value of 1 or 0, meaning
Figure 411303DEST_PATH_IMAGE063
Whether individual electricity price blocks cover a day
Figure 550160DEST_PATH_IMAGE008
If it is covered, then
Figure 195905DEST_PATH_IMAGE064
Otherwise
Figure 711200DEST_PATH_IMAGE065
；
Figure 430895DEST_PATH_IMAGE066
Is the retailer to the user
Figure 740653DEST_PATH_IMAGE004
In that
Figure 811377DEST_PATH_IMAGE063
A cost of electricity purchase in time, the cost including costs from forward contracts and spot markets;
Figure 864784DEST_PATH_IMAGE067
is the retailer to the user
Figure 766881DEST_PATH_IMAGE004
Risk of premium;
the elastic constraint model of the power demand is as follows:
Figure 247541DEST_PATH_IMAGE068
Figure 805561DEST_PATH_IMAGE069
Figure 397079DEST_PATH_IMAGE070
wherein the content of the first and second substances,
Figure 91366DEST_PATH_IMAGE071
is a nominal retail price;
Figure 8506DEST_PATH_IMAGE072
，
Figure 788244DEST_PATH_IMAGE072
and
Figure 245770DEST_PATH_IMAGE072
is a parameter;
the power distribution network operation constraint model is as follows:
Figure 60142DEST_PATH_IMAGE073
Figure 882604DEST_PATH_IMAGE074
Figure 149638DEST_PATH_IMAGE075
Figure 348538DEST_PATH_IMAGE076
Figure 17417DEST_PATH_IMAGE077
Figure 10780DEST_PATH_IMAGE078
wherein, the first and the second end of the pipe are connected with each other,
Figure 328892DEST_PATH_IMAGE079
is a power distribution coefficient, indicates that the node is
Figure 65903DEST_PATH_IMAGE080
On the line in response to actual power changes
Figure 589289DEST_PATH_IMAGE081
The relative change in active power that occurs;
Figure 19133DEST_PATH_IMAGE082
is a circuit
Figure 260758DEST_PATH_IMAGE081
The power limit of (d);
Figure 535882DEST_PATH_IMAGE083
and
Figure 179353DEST_PATH_IMAGE084
respectively a node set and a line set of the power distribution network;
Figure 576836DEST_PATH_IMAGE085
and
Figure 305758DEST_PATH_IMAGE086
are binary vectors whose elements are 0 or 1.
5. The power package recommendation method of claim 4, wherein the determining an optimal power usage pattern package comprises:
determining the optimal power utilization pattern package by utilizing a preset end user power utilization expectation model, a retailer power purchase cost model, a risk price premium model in the retail price and an expected retail income model of the retailer;
wherein the end user's power usage expectation model is:
Figure 384572DEST_PATH_IMAGE087
wherein the content of the first and second substances,
Figure 882550DEST_PATH_IMAGE088
time terminal user
Figure 654197DEST_PATH_IMAGE004
In the power consumption mode of
Figure 604835DEST_PATH_IMAGE008
An element;
the electricity purchase cost model of the retailer is as follows:
Figure 549657DEST_PATH_IMAGE089
Figure 902141DEST_PATH_IMAGE090
wherein the content of the first and second substances,
Figure 844689DEST_PATH_IMAGE091
is the number of forward contracts the retailer has signed;
Figure 282624DEST_PATH_IMAGE092
and
Figure 703241DEST_PATH_IMAGE093
respectively indicating to the retailer to make a forward contract
Figure 175811DEST_PATH_IMAGE094
Quantity and price level of;
Figure 289260DEST_PATH_IMAGE095
is a weighting factor between the expected revenue of the retailer and the profit risk,
Figure 276808DEST_PATH_IMAGE096
；
Figure 501116DEST_PATH_IMAGE097
representing retail risks arising from differences between forward contracts and end-user load expectations;
Figure 562613DEST_PATH_IMAGE098
representing a risk value VaR for calculating a conditional risk value CVaR;
Figure 846964DEST_PATH_IMAGE099
is a given confidence level;
Figure 259490DEST_PATH_IMAGE100
represents the number of samples;
Figure 287489DEST_PATH_IMAGE101
is the cost of electricity purchased by the retailer from the spot market;
Figure 469072DEST_PATH_IMAGE102
is a binary variable, if
Figure 222526DEST_PATH_IMAGE103
To represent
Figure 122349DEST_PATH_IMAGE008
Time belonging to a long term contract
Figure 688460DEST_PATH_IMAGE094
A lead time of (c);
Figure 990128DEST_PATH_IMAGE104
is that
Figure 350702DEST_PATH_IMAGE008
Spot price in time;
Figure 737821DEST_PATH_IMAGE105
time terminal user
Figure 169940DEST_PATH_IMAGE004
In that
Figure 60535DEST_PATH_IMAGE008
The desire to use electricity;
Figure 857590DEST_PATH_IMAGE106
representing end users
Figure 466426DEST_PATH_IMAGE004
Expectation of daily electricity usage;
a risk premium model in the retail price:
Figure 639918DEST_PATH_IMAGE107
the expected retail revenue model for the retailer:
Figure 650600DEST_PATH_IMAGE108
Figure 618556DEST_PATH_IMAGE109
wherein the content of the first and second substances,
Figure 777005DEST_PATH_IMAGE110
indicating a retail profit for the retailer;
Figure 488609DEST_PATH_IMAGE111
indicating the profitability of the retailer's demand.
6. The method of claim 1, further comprising:
linearize retail risk due to differences between the forward contract and the end user load expectations:
Figure 353796DEST_PATH_IMAGE112
Figure 492654DEST_PATH_IMAGE113
Figure 76082DEST_PATH_IMAGE114
wherein the content of the first and second substances,
Figure 325798DEST_PATH_IMAGE115
is for linearization
Figure 311071DEST_PATH_IMAGE116
But an auxiliary variable introduced.
7. The utility model provides an electric power package recommendation device based on load curve cluster analysis which characterized in that includes:
the system comprises an acquisition module, a data processing module and a data processing module, wherein the acquisition module is used for acquiring historical electricity consumption data of terminal users and analyzing a typical electricity consumption mode of each terminal user by using a DBSCAN clustering algorithm;
the processing module is used for optimizing the combination in the typical electricity utilization mode of the terminal user according to the time-of-use electricity price structure and the electricity price level by using a mixed integer nonlinear programming method, and determining an optimal electricity utilization mode package;
the execution module is used for recommending the optimal power consumption mode package for each terminal user to the terminal user;
wherein the processing module is used for utilizing DBSCANCalculating the electricity utilization mode of the terminal user by a clustering algorithm formula
Figure 683147DEST_PATH_IMAGE001
Comprises the following steps:
Figure 753871DEST_PATH_IMAGE002
wherein the vector
Figure 807277DEST_PATH_IMAGE003
Representing the end user
Figure 647057DEST_PATH_IMAGE004
In that
Figure 127717DEST_PATH_IMAGE005
The electricity consumption vector of a day is calculated,
Figure 420158DEST_PATH_IMAGE006
it is shown that the operation of the standardization,
Figure 277256DEST_PATH_IMAGE007
to (1) a
Figure 535324DEST_PATH_IMAGE008
An element representing the end user
Figure 186885DEST_PATH_IMAGE004
In that
Figure 232202DEST_PATH_IMAGE008
The first day
Figure 627411DEST_PATH_IMAGE008
The power consumption of each time section is the ratio of the power consumption of the user on the day,
Figure 176204DEST_PATH_IMAGE009
a time period representing historical power usage data,
Figure 264246DEST_PATH_IMAGE010
representing a collection of typical power usage patterns, TP representing a typical power usage pattern for an end user,
Figure 593596DEST_PATH_IMAGE011
representing the L2 norm of the vector.
8. The power package recommendation device of claim 7,
the execution module is used for optimizing the power utilization mode of the terminal user according to the time-of-use power price structure and the power price level by using an optimization objective function of mixed integer nonlinear programming;
the optimization objective function is:
Figure 792496DEST_PATH_IMAGE012
wherein the content of the first and second substances,
Figure 195796DEST_PATH_IMAGE013
representing a total electricity cost for the end user;
Figure 454739DEST_PATH_IMAGE014
represents the set of all end users;
Figure 209068DEST_PATH_IMAGE015
representing a decision period;
Figure 946080DEST_PATH_IMAGE016
representing the end user
Figure 469465DEST_PATH_IMAGE004
On the first day
Figure 961626DEST_PATH_IMAGE008
(ii) a desire for electricity usage for a time period;
Figure 937673DEST_PATH_IMAGE017
representing the end user
Figure 478375DEST_PATH_IMAGE004
On the first day
Figure 121846DEST_PATH_IMAGE008
The electricity prices of the individual time periods,
Figure 457013DEST_PATH_IMAGE018
representing the end user
Figure 185934DEST_PATH_IMAGE004
On the first day
Figure 327066DEST_PATH_IMAGE008
Elasticity of electricity demand for each time period with respect to real-time electricity prices.
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